// @version=5 strategy('DAHUJB Johns Indicator', 'Johns Indicator', true, precision=2, initial_capital = 100000, default_qty_type=strategy.percent_of_equity, process_orders_on_close = true, calc_on_every_tick = false, commission_value = 0.03, max_bars_back = 2000) // ====================== // ==== Custom Types ==== // ====================== type Settings float source int neighborsCount int maxBarsBack int featureCount int colorCompression bool showExits bool useDynamicExits type Label int long int short int neutral type FeatureArrays array f1 array f2 array f3 array f4 array f5 type FeatureSeries float f1 float f2 float f3 float f4 float f5 type MLModel int firstBarIndex array trainingLabels int loopSize float lastDistance array distancesArray array predictionsArray int prediction type FilterSettings bool useVolatilityFilter bool useRegimeFilter bool useAdxFilter float regimeThreshold int adxThreshold type Filter bool volatility bool regime bool adx // ========================== // ==== Helper Functions ==== // ========================== normalize(series float src, float min, float max) => var _historicMin = 10e10 var _historicMax = -10e10 _historicMin := math.min(nz(src, _historicMin), _historicMin) _historicMax := math.max(nz(src, _historicMax), _historicMax) min + (max - min) * (src - _historicMin) / math.max(_historicMax - _historicMin, 10e-10) rescale(series float src, float oldMin, float oldMax, float newMin, float newMax) => newMin + (newMax - newMin) * (src - oldMin) / math.max(oldMax - oldMin, 10e-10) n_wt(series float src, simple int n1=10, simple int n2=11) => ema1 = ta.ema(src, n1) ema2 = ta.ema(math.abs(src - ema1), n1) ci = (src - ema1) / (0.015 * ema2) wt1 = ta.ema(ci, n2) // tci wt2 = ta.sma(wt1, 4) normalize(wt1 - wt2, 0, 1) n_adx(series float highSrc, series float lowSrc, series float closeSrc, simple int n1) => length = n1 th = 20 tr = math.max(math.max(highSrc - lowSrc, math.abs(highSrc - nz(closeSrc[1]))), math.abs(lowSrc - nz(closeSrc[1]))) directionalMovementPlus = highSrc - nz(highSrc[1]) > nz(lowSrc[1]) - lowSrc ? math.max(highSrc - nz(highSrc[1]), 0) : 0 negMovement = nz(lowSrc[1]) - lowSrc > highSrc - nz(highSrc[1]) ? math.max(nz(lowSrc[1]) - lowSrc, 0) : 0 trSmooth = 0.0 trSmooth := nz(trSmooth[1]) - nz(trSmooth[1]) / length + tr smoothDirectionalMovementPlus = 0.0 smoothDirectionalMovementPlus := nz(smoothDirectionalMovementPlus[1]) - nz(smoothDirectionalMovementPlus[1]) / length + directionalMovementPlus smoothnegMovement = 0.0 smoothnegMovement := nz(smoothnegMovement[1]) - nz(smoothnegMovement[1]) / length + negMovement diPositive = smoothDirectionalMovementPlus / trSmooth * 100 diNegative = smoothnegMovement / trSmooth * 100 dx = math.abs(diPositive - diNegative) / (diPositive + diNegative) * 100 adx = ta.rma(dx, length) rescale(adx, 0, 100, 0, 1) n_rsi(series float src, simple int n1, simple int n2) => rescale(ta.ema(ta.rsi(src, n1), n2), 0, 100, 0, 1) n_cci(series float src, simple int n1, simple int n2) => normalize(ta.ema(ta.cci(src, n1), n2), 0, 1) series_from(feature_string, _close, _high, _low, _hlc3, f_paramA, f_paramB) => switch feature_string "RSI" => n_rsi(_close, f_paramA, f_paramB) "WT" => n_wt(_hlc3, f_paramA, f_paramB) "CCI" => n_cci(_close, f_paramA, f_paramB) "ADX" => n_adx(_high, _low, _close, f_paramA) get_lorentzian_distance(int i, int featureCount, FeatureSeries featureSeries, FeatureArrays featureArrays) => switch featureCount 5 => math.log(1+math.abs(featureSeries.f1 - featureSeries.f1[i])) + math.log(1+math.abs(featureSeries.f2 - featureSeries.f2[i])) + math.log(1+math.abs(featureSeries.f3 - featureSeries.f3[i])) + math.log(1+math.abs(featureSeries.f4 - featureSeries.f4[i])) + math.log(1+math.abs(featureSeries.f5 - featureSeries.f5[i])) 4 => math.log(1+math.abs(featureSeries.f1 - featureSeries.f1[i])) + math.log(1+math.abs(featureSeries.f2 - featureSeries.f2[i])) + math.log(1+math.abs(featureSeries.f3 - featureSeries.f3[i])) + math.log(1+math.abs(featureSeries.f4 - featureSeries.f4[i])) 3 => math.log(1+math.abs(featureSeries.f1 - featureSeries.f1[i])) + math.log(1+math.abs(featureSeries.f2 - featureSeries.f2[i])) + math.log(1+math.abs(featureSeries.f3 - featureSeries.f3[i])) 2 => math.log(1+math.abs(featureSeries.f1 - featureSeries.f1[i])) + math.log(1+math.abs(featureSeries.f2 - featureSeries.f2[i])) // ================ // ==== Inputs ==== // ================ // Settings Object: General User-Defined Inputs Settings settings = Settings.new( input.source(title='Source', defval=close, group="General Settings", tooltip="Source of the input data"), input.int(title='Neighbors Count', defval=8, group="General Settings", minval=1, maxval=100, step=1, tooltip="Number of neighbors to consider"), input.int(title="Max Bars Back", defval=2000, group="General Settings", tooltip = "Use this to increase or reduce the range of backtesting. If script use much time to load, reduce this"), input.int(title="Feature Count", defval=5, group="Feature Engineering", minval=2, maxval=5, tooltip="Number of features to use for ML predictions."), input.int(title="Color Compression", defval=1, group="General Settings", minval=1, maxval=10, tooltip="Compression factor for adjusting the intensity of the color scale."), input.bool(title="Show Default Exits", defval=false, group="General Settings", tooltip="Default exits occur exactly 4 bars after an entry signal. This corresponds to the predefined length of a trade during the model's training process.", inline="exits"), input.bool(title="Use Dynamic Exits", defval=false, group="General Settings", tooltip="Dynamic exits attempt to let profits ride by dynamically adjusting the exit threshold based on kernel regression logic.", inline="exits") ) // Trade Stats Settings // Note: The trade stats section is NOT intended to be used as a replacement for proper backtesting. It is intended to be used for calibration purposes only. // showTradeStats = input.bool(false, 'Show Trade Stats', tooltip='Displays the trade stats for a given configuration. Useful for optimizing the settings in the Feature Engineering section. This should NOT replace backtesting and should be used for calibration purposes only. Early Signal Flips represent instances where the model changes signals before 4 bars elapses; high values can indicate choppy (ranging) market conditions.', group="General Settings") // useWorstCase = input.bool(false, "Use Worst Case Estimates", tooltip="Whether to use the worst case scenario for backtesting. This option can be useful for creating a conservative estimate that is based on close prices only, thus avoiding the e?ects of intrabar repainting. This option assumes that the user does not enter when the signal first appears and instead waits for the bar to close as confirmation. On larger timeframes, this can mean entering after a large move has already occurred. Leaving this option disabled is generally better for those that use this indicator as a source of confluence and prefer estimates that demonstrate discretionary mid-bar entries. Leaving this option enabled may be more consistent with traditional backtesting results.", group="General Settings") // Settings object for user-defined settings FilterSettings filterSettings = FilterSettings.new( input.bool(title="Use Volatility Filter", defval=true, tooltip="Whether to use the volatility filter.", group="Filters"), input.bool(title="Use Regime Filter", defval=true, group="Filters", inline="regime"), input.bool(title="Use ADX Filter", defval=false, group="Filters", inline="adx"), input.float(title="Threshold", defval=-0.1, minval=-10, maxval=10, step=0.1, tooltip="Whether to use the trend detection filter. Threshold for detecting Trending/Ranging markets.", group="Filters", inline="regime"), input.int(title="Threshold", defval=20, minval=0, maxval=100, step=1, tooltip="Whether to use the ADX filter. Threshold for detecting Trending/Ranging markets.", group="Filters", inline="adx") ) filter_volatility(simple int minLength=1, simple int maxLength=10, bool useVolatilityFilter) => recentAtr = ta.atr(minLength) historicalAtr = ta.atr(maxLength) useVolatilityFilter ? recentAtr > historicalAtr : true regime_filter(series float src=ohlc4, float threshold, bool useRegimeFilter) => // Calculate the slope of the curve. value1 = 0.0 value2 = 0.0 klmf = 0.0 value1 := 0.2 * (src - src[1]) + 0.8 * nz(value1[1]) value2 := 0.1 * (high - low) + 0.8 * nz(value2[1]) omega = math.abs(value1 / value2) alpha = (-math.pow(omega,2) + math.sqrt(math.pow(omega, 4) + 16 * math.pow(omega,2))) / 8 klmf := alpha * src + (1 - alpha) * nz(klmf[1]) absCurveSlope = math.abs(klmf - klmf[1]) exponentialAverageAbsCurveSlope = 1.0 * ta.ema(absCurveSlope, 200) normalized_slope_decline = (absCurveSlope - exponentialAverageAbsCurveSlope) / exponentialAverageAbsCurveSlope // Calculate the slope of the curve. useRegimeFilter ? normalized_slope_decline >= threshold : true filter_adx(series float src=close, simple int length=14, int adxThreshold, bool useAdxFilter) => tr = math.max(math.max(high - low, math.abs(high - nz(src[1]))), math.abs(low - nz(src[1]))) directionalMovementPlus = high - nz(high[1]) > nz(low[1]) - low ? math.max(high - nz(high[1]), 0) : 0 negMovement = nz(low[1]) - low > high - nz(high[1]) ? math.max(nz(low[1]) - low, 0) : 0 trSmooth = 0.0 trSmooth := nz(trSmooth[1]) - nz(trSmooth[1]) / length + tr smoothDirectionalMovementPlus = 0.0 smoothDirectionalMovementPlus := nz(smoothDirectionalMovementPlus[1]) - nz(smoothDirectionalMovementPlus[1]) / length + directionalMovementPlus smoothnegMovement = 0.0 smoothnegMovement := nz(smoothnegMovement[1]) - nz(smoothnegMovement[1]) / length + negMovement diPositive = smoothDirectionalMovementPlus / trSmooth * 100 diNegative = smoothnegMovement / trSmooth * 100 dx = math.abs(diPositive - diNegative) / (diPositive + diNegative) * 100 adx = ta.rma(dx, length) useAdxFilter ? adx > adxThreshold : true // Filter object for filtering the ML predictions Filter filter = Filter.new( filter_volatility(1, 10, filterSettings.useVolatilityFilter), regime_filter(ohlc4, filterSettings.regimeThreshold, filterSettings.useRegimeFilter), filter_adx(settings.source, 14, filterSettings.adxThreshold, filterSettings.useAdxFilter) ) // Feature Variables: User-Defined Inputs for calculating Feature Series. f1_string = input.string(title="Feature 1", options=["RSI", "WT", "CCI", "ADX"], defval="RSI", inline = "01", tooltip="The first feature to use for ML predictions.", group="Feature Engineering") f1_paramA = input.int(title="Parameter A", tooltip="The primary parameter of feature 1.", defval=14, inline = "02", group="Feature Engineering") f1_paramB = input.int(title="Parameter B", tooltip="The secondary parameter of feature 2 (if applicable).", defval=1, inline = "02", group="Feature Engineering") f2_string = input.string(title="Feature 2", options=["RSI", "WT", "CCI", "ADX"], defval="WT", inline = "03", tooltip="The second feature to use for ML predictions.", group="Feature Engineering") f2_paramA = input.int(title="Parameter A", tooltip="The primary parameter of feature 2.", defval=10, inline = "04", group="Feature Engineering") f2_paramB = input.int(title="Parameter B", tooltip="The secondary parameter of feature 2 (if applicable).", defval=11, inline = "04", group="Feature Engineering") f3_string = input.string(title="Feature 3", options=["RSI", "WT", "CCI", "ADX"], defval="CCI", inline = "05", tooltip="The third feature to use for ML predictions.", group="Feature Engineering") f3_paramA = input.int(title="Parameter A", tooltip="The primary parameter of feature 3.", defval=20, inline = "06", group="Feature Engineering") f3_paramB = input.int(title="Parameter B", tooltip="The secondary parameter of feature 3 (if applicable).", defval=1, inline = "06", group="Feature Engineering") f4_string = input.string(title="Feature 4", options=["RSI", "WT", "CCI", "ADX"], defval="ADX", inline = "07", tooltip="The fourth feature to use for ML predictions.", group="Feature Engineering") f4_paramA = input.int(title="Parameter A", tooltip="The primary parameter of feature 4.", defval=20, inline = "08", group="Feature Engineering") f4_paramB = input.int(title="Parameter B", tooltip="The secondary parameter of feature 4 (if applicable).", defval=2, inline = "08", group="Feature Engineering") f5_string = input.string(title="Feature 5", options=["RSI", "WT", "CCI", "ADX"], defval="RSI", inline = "09", tooltip="The fifth feature to use for ML predictions.", group="Feature Engineering") f5_paramA = input.int(title="Parameter A", tooltip="The primary parameter of feature 5.", defval=9, inline = "10", group="Feature Engineering") f5_paramB = input.int(title="Parameter B", tooltip="The secondary parameter of feature 5 (if applicable).", defval=1, inline = "10", group="Feature Engineering") // FeatureSeries Object: Calculated Feature Series based on Feature Variables featureSeries = FeatureSeries.new( series_from(f1_string, close, high, low, hlc3, f1_paramA, f1_paramB), // f1 series_from(f2_string, close, high, low, hlc3, f2_paramA, f2_paramB), // f2 series_from(f3_string, close, high, low, hlc3, f3_paramA, f3_paramB), // f3 series_from(f4_string, close, high, low, hlc3, f4_paramA, f4_paramB), // f4 series_from(f5_string, close, high, low, hlc3, f5_paramA, f5_paramB) // f5 ) var f1Array = array.new_float() var f2Array = array.new_float() var f3Array = array.new_float() var f4Array = array.new_float() var f5Array = array.new_float() array.push(f1Array, featureSeries.f1) array.push(f2Array, featureSeries.f2) array.push(f3Array, featureSeries.f3) array.push(f4Array, featureSeries.f4) array.push(f5Array, featureSeries.f5) // FeatureArrays Object: Storage of the calculated FeatureArrays into a single object featureArrays = FeatureArrays.new( f1Array, // f1 f2Array, // f2 f3Array, // f3 f4Array, // f4 f5Array // f5 ) // Label Object: Used for classifying historical data as training data for the ML Model Label direction_s = Label.new( long=1, short=-1, neutral=0 ) // Derived from General Settings maxBarsBackIndex = last_bar_index >= settings.maxBarsBack ? last_bar_index - settings.maxBarsBack : 0 // EMA Settings useEmaFilter = input.bool(title="Use EMA Filter", defval=false, group="Filters", inline="ema") emaPeriod = input.int(title="Period", defval=200, minval=1, step=1, group="Filters", inline="ema", tooltip="The period of the EMA used for the EMA Filter.") isEmaUptrend = useEmaFilter ? close > ta.ema(close, emaPeriod) : true isEmaDowntrend = useEmaFilter ? close < ta.ema(close, emaPeriod) : true useSmaFilter = input.bool(title="Use SMA Filter", defval=false, group="Filters", inline="sma") smaPeriod = input.int(title="Period", defval=200, minval=1, step=1, group="Filters", inline="sma", tooltip="The period of the SMA used for the SMA Filter.") isSmaUptrend = useSmaFilter ? close > ta.sma(close, smaPeriod) : true isSmaDowntrend = useSmaFilter ? close < ta.sma(close, smaPeriod) : true useKernelFilter = input.bool(true, "Trade with Kernel", group="Kernel Settings", inline="kernel") showKernelEstimate = input.bool(false, "Show Kernel Estimate", group="Kernel Settings", inline="kernel") useKernelSmoothing = input.bool(false, "Enhance Kernel Smoothing", tooltip="Uses a crossover based mechanism to smoothen kernel color changes. This often results in less color transitions overall and may result in more ML entry signals being generated.", inline='1', group='Kernel Settings') h = input.int(8, 'Lookback Window', minval=3, tooltip='The number of bars used for the estimation. This is a sliding value that represents the most recent historical bars. Recommended range: 3-50', group="Kernel Settings", inline="kernel") r = input.float(8., 'Relative Weighting', step=0.25, tooltip='Relative weighting of time frames. As this value approaches zero, the longer time frames will exert more influence on the estimation. As this value approaches infinity, the behavior of the Rational Quadratic Kernel will become identical to the Gaussian kernel. Recommended range: 0.25-25', group="Kernel Settings", inline="kernel") x = input.int(25, "Regression Level", tooltip='Bar index on which to start regression. Controls how tightly fit the kernel estimate is to the data. Smaller values are a tighter fit. Larger values are a looser fit. Recommended range: 2-25', group="Kernel Settings", inline="kernel") lag = input.int(2, "Lag", tooltip="Lag for crossover detection. Lower values result in earlier crossovers. Recommended range: 1-2", inline='1', group='Kernel Settings') // Display Settings showBarColors = input.bool(false, "Show Bar Colors", tooltip="Whether to show the bar colors.", group="Display Settings") showBarPredictions = input.bool(defval = false, title = "Show Bar Prediction Values", tooltip = "Will show the ML model's evaluation of each bar as an integer.", group="Display Settings") useAtrO?set = input.bool(defval = false, title = "Use ATR O?set", tooltip = "Will use the ATR o?set instead of the bar prediction o?set.", group="Display Settings") barPredictionsO?set = input.float(0, "Bar Prediction O?set", minval=0, tooltip="The o?set of the bar predictions as a percentage from the bar high or close.", group="Display Settings") // ================================= // ==== Next Bar Classification ==== // ================================= // This model specializes specifically in predicting the direction_s of price action over the course of the next 4 bars. // To avoid complications with the ML model, this value is hardcoded to 4 bars but support for other training lengths may be added in the future. src = settings.source y_train_series = src[4] < src[0] ? direction_s.short : src[4] > src[0] ? direction_s.long : direction_s.neutral var y_train_array = array.new_int(0) // Variables used for ML Logic var predictions = array.new_float(0) var prediction = 0. var signal = direction_s.neutral var distances = array.new_float(0) array.push(y_train_array, y_train_series) // ========================= // ==== Core ML Logic ==== // ========================= float lastDistance = na size = math.min(settings.maxBarsBack-1, array.size(y_train_array)-1) sizeLoop = math.min(settings.maxBarsBack-1, size) if bar_index >= 5 //{ for i = 4 to sizeLoop //{ d = get_lorentzian_distance(i, settings.featureCount, featureSeries, featureArrays) if ( ( d <= lastDistance or not lastDistance ) and i%4 == 0 )//{ lastDistance := d array.push(distances, d) array.push(predictions, math.round(array.get(y_train_array, i - 4))) if array.size(predictions) > settings.neighborsCount //{ lastDistance := array.get(distances, math.round(settings.neighborsCount*3/4)) array.shift(distances) array.shift(predictions) //} //} //} prediction := array.sum(predictions) //} // ============================ // ==== Prediction Filters ==== // ============================ // User Defined Filters: Used for adjusting the frequency of the ML Model's predictions filter_all = filter.volatility and filter.regime and filter.adx // Filtered Signal: The model's prediction of future price movement direction_s with user-defined filters applied signal := prediction > 0 and filter_all ? direction_s.long : prediction < 0 and filter_all ? direction_s.short : nz(signal[1]) // Bar-Count Filters: Represents strict filters based on a pre-defined holding period of 4 bars var int barsHeld = 0 barsHeld := ta.change(signal) ? 0 : barsHeld + 1 isHeldFourBars = barsHeld == 4 isHeldLessThanFourBars = 0 < barsHeld and barsHeld < 4 isDi?erentSignalType = ta.change(signal) isEarlySignalFlip = ta.change(signal) and (ta.change(signal[1]) or ta.change(signal[2]) or ta.change(signal[3])) isBuySignal = signal == direction_s.long and isEmaUptrend and isSmaUptrend isSellSignal = signal == direction_s.short and isEmaDowntrend and isSmaDowntrend isLastSignalBuy = signal[4] == direction_s.long and isEmaUptrend[4] and isSmaUptrend[4] isLastSignalSell = signal[4] == direction_s.short and isEmaDowntrend[4] and isSmaDowntrend[4] isNewBuySignal = isBuySignal and isDi?erentSignalType isNewSellSignal = isSellSignal and isDi?erentSignalType rationalQuadratic(series float _src, simple int _lookback, simple float _relativeWeight, simple int startAtBar) => float _currentWeight = 0. float _cumulativeWeight = 0. _size = array.size(array.from(_src)) for i = 0 to _size + startAtBar y = _src[i] w = math.pow(1 + (math.pow(i, 2) / ((math.pow(_lookback, 2) * 2 * _relativeWeight))), - _relativeWeight) _currentWeight += y*w _cumulativeWeight += w yhat = _currentWeight / _cumulativeWeight yhat gaussian(series float _src, simple int _lookback, simple int startAtBar) => float _currentWeight = 0. float _cumulativeWeight = 0. _size = array.size(array.from(_src)) for i = 0 to _size + startAtBar y = _src[i] w = math.exp(-math.pow(i, 2) / (2 * math.pow(_lookback, 2))) _currentWeight += y*w _cumulativeWeight += w yhat = _currentWeight / _cumulativeWeight yhat c_green = color.new(#009988, 20) c_red = color.new(#CC3311, 20) transparent = color.new(#000000, 100) yhat1 = rationalQuadratic(settings.source, h, r, x) yhat2 = gaussian(settings.source, h-lag, x) kernelEstimate = yhat1 // Kernel Rates of Change bool wasBearishRate = yhat1[2] > yhat1[1] bool wasBullishRate = yhat1[2] < yhat1[1] bool isBearishRate = yhat1[1] > yhat1 bool isBullishRate = yhat1[1] < yhat1 isBearishChange = isBearishRate and wasBullishRate isBullishChange = isBullishRate and wasBearishRate // Kernel Crossovers bool isBullishCrossAlert = ta.crossover(yhat2, yhat1) bool isBearishCrossAlert = ta.crossunder(yhat2, yhat1) bool isBullishSmooth = yhat2 >= yhat1 bool isBearishSmooth = yhat2 <= yhat1 // Kernel Colors color colorByCross = isBullishSmooth ? c_green : c_red color colorByRate = isBullishRate ? c_green : c_red color plotColor = showKernelEstimate ? (useKernelSmoothing ? colorByCross : colorByRate) : transparent plot(kernelEstimate, color=plotColor, linewidth=2, title="Kernel Regression Estimate", display = display.all - display.price_scale - display.status_line) // Alert Variables bool alertBullish = useKernelSmoothing ? isBullishCrossAlert : isBullishChange bool alertBearish = useKernelSmoothing ? isBearishCrossAlert : isBearishChange // Bullish and Bearish Filters based on Kernel isBullish = useKernelFilter ? (useKernelSmoothing ? isBullishSmooth : isBullishRate) : true isBearish = useKernelFilter ? (useKernelSmoothing ? isBearishSmooth : isBearishRate) : true // =========================== // ==== Entries and Exits ==== // =========================== // Entry Conditions: Booleans for ML Model Position Entries startLongTrade = isNewBuySignal and isBullish and isEmaUptrend and isSmaUptrend startShortTrade = isNewSellSignal and isBearish and isEmaDowntrend and isSmaDowntrend lastSignalWasBullish = ta.barssince(startLongTrade) < ta.barssince(startShortTrade) lastSignalWasBearish = ta.barssince(startShortTrade) < ta.barssince(startLongTrade) barsSinceRedEntry = ta.barssince(startShortTrade) barsSinceRedExit = ta.barssince(alertBullish) barsSinceGreenEntry = ta.barssince(startLongTrade) barsSinceGreenExit = ta.barssince(alertBearish) isValidShortExit = barsSinceRedExit > barsSinceRedEntry isValidLongExit = barsSinceGreenExit > barsSinceGreenEntry endLongTradeDynamic = (isBearishChange and isValidLongExit[1]) endShortTradeDynamic = (isBullishChange and isValidShortExit[1]) plot(yhat1, title="yhat1", display = display.data_window) plot(yhat2, title="yhat2", display = display.data_window) // Fixed Exit Conditions: Booleans for ML Model Position Exits based on a Bar-Count Filters endLongTradeStrict = ((isHeldFourBars and isLastSignalBuy) or (isHeldLessThanFourBars and isNewSellSignal and isLastSignalBuy)) and startLongTrade[4] endShortTradeStrict = ((isHeldFourBars and isLastSignalSell) or (isHeldLessThanFourBars and isNewBuySignal and isLastSignalSell)) and startShortTrade[4] isDynamicExitValid = not useEmaFilter and not useSmaFilter and not useKernelSmoothing endLongTrade = settings.useDynamicExits and isDynamicExitValid ? endLongTradeDynamic : endLongTradeStrict endShortTrade = settings.useDynamicExits and isDynamicExitValid ? endShortTradeDynamic : endShortTradeStrict atrSpaced = useAtrO?set ? ta.atr(1) : na compressionFactor = settings.neighborsCount / settings.colorCompression c_pred = prediction > 0 ? color.from_gradient(prediction, 0, compressionFactor, #787b86, #009988) : prediction <= 0 ? color.from_gradient(prediction, -compressionFactor, 0, #CC3311, #787b86) : na c_label = showBarPredictions ? c_pred : na c_bars = showBarColors ? color.new(c_pred, 50) : na x_val = bar_index y_val = useAtrO?set ? prediction > 0 ? high + atrSpaced: low - atrSpaced : prediction > 0 ? high + hl2*barPredictionsO?set/20 : low - hl2*barPredictionsO?set/30 label.new(x_val, y_val, str.tostring(prediction), xloc.bar_index, yloc.price, color.new(color.white, 100), label.style_label_up, c_label, size.normal, text.align_left) barcolor(showBarColors ? color.new(c_pred, 50) : na) // ============================================================================= ============================================================================= ============================================================================= ======================================================= // ====================================== Strategy code ================================================================ // ------ Inputs for calculating our amount position -------- initial_actual_capital = input.float(defval=10000, title = "Enter initial/current capital", group = "============ Position amount calculator ===========") risk_c = input.float(2.5, '% account risk per trade', step=1, group = "============ Position amount calculator ===========", tooltip = "Percentage of total account to risk per trade. The USD value that should be used to risk the inserted percentage of the account. Appears as a yellow number in the upper left corner") // ------ Date filter --------- initial_date = input.time(title="Initial date", defval=timestamp("10 Feb 2014 13:30 +0000"), group="============ Time filter ============ ", tooltip="Enter the start date and time of the strategy") final_date = input.time(title="Final date", defval=timestamp("01 Jan 2030 19:30 +0000"), group="============ Time filter ============ ", tooltip="Enter the end date and time of the strategy") dateFilter(int st, int et) => time >= st and time <= et colorDate = input.bool(defval=false, title="Date background", tooltip = "Add color to the period of time of the strategy tester") bgcolor(colorDate and dateFilter(initial_date, final_date) ? color.new(color.blue, transp=90) : na) date = dateFilter(initial_date, final_date) // ------ Session limits ------ timeSession = input.session(title="Time session", defval="0000-2400", group="============ Time filter ============ ", tooltip="Session time to operate. It may be di?erent depending on your time zone, you have to find the correct hours manually.") colorBG = input.bool(title="Session background", defval=false, tooltip = "Add color to session time background") inSession(sess) => na(time(timeframe.period, sess + ':1234567')) == false bgcolor(inSession(timeSession) and colorBG ? color.rgb(0, 38, 255, 84) : na) // ============================================================================= ================================================================== // ------------ Super Trend ---------- atrPeriod = input(9, "ATR Length SuperTrend", group = "========= Super Trend filter ==========") factor = input.float(2.5, "Factor SuperTrend", step = 0.05, group = "========= Super Trend filter ==========") [supertrend, direction_supertrend] = ta.supertrend(factor, atrPeriod) show_supertrend = input.bool(defval = false, title="Show supertrend ?", group = "============ Appearance ============") bodyMiddle = plot(show_supertrend ? ((open + close) / 2) : na, display=display.none) upTrend = plot(show_supertrend and direction_supertrend < 0 ? supertrend : na, "Up Trend", color = color.green, style=plot.style_linebr, display = display.all - display.status_line) downTrend = plot(show_supertrend and direction_supertrend > 0 ? supertrend : na, "Down Trend", color = color.red, style=plot.style_linebr, display = display.all - display.status_line) fill(bodyMiddle, upTrend, color.new(color.green, 95), fillgaps=false, title = "Supertrend background") fill(bodyMiddle, downTrend, color.new(color.red, 95), fillgaps=false, title = "Supertrend background") up_trend_plot = direction_supertrend < 0 down_trend_plot = direction_supertrend > 0 // ----------- Ema ---------------------- ema = input.int(200, title='Ema length', minval=1, maxval=500, group = "============ Trend ============") ema200 = ta.ema(close, ema) bullish = close > ema200 bearish = close < ema200 show_ema = input.bool(defval=true, title="Show ema ?", group = "============ Appearance ============") plot(show_ema ? ema200 : na, title = "Ema", color=color.white, linewidth=2, display = display.all - display.status_line - display.price_scale) // ============================================================================= ================================================================== source_atr = input(close, title='Source', group = "================== Stop loss ==================", inline = "A") length_atr = input.int(14, minval=1, title='Period', group = "================== Stop loss ==================" , inline = "A") multiplier = input.float(1.5, minval=0.1, step=0.1, title='Atr multiplier', group = "================== Stop loss ==================", inline = "A", tooltip = "Defines the stop loss distance based on the Atr stop loss indicator") show_stoploss = input.bool(defval = true, title = "Show stop loss ?", group = "============ Appearance ============") var float shortStopLoss = na var float longStopLoss = na var float atr_past_candle_long = na var float atr_past_candle_short = na candle_of_stoploss = input.string(defval = "Current candle", title = "Stop loss source for atr stoploss", group = "================== Stop loss ==================", options = ["Current candle","Past candle"]) if candle_of_stoploss == "Current candle" shortStopLoss := source_atr + ta.atr(length_atr) * multiplier longStopLoss := source_atr - ta.atr(length_atr) * multiplier if candle_of_stoploss == "Past candle" shortStopLoss := close[1] + ta.atr(length_atr)[1] * multiplier[1] longStopLoss := close[1] - ta.atr(length_atr)[1] * multiplier[1] // --------- Stop loss based in last swing high/low -------- high_bars = input.int(defval = 10, title = "Highest price bars: ", group = "================== Stop loss ==================") low_bars = input.int(defval = 10, title = "Lowest price bars: ", group = "================== Stop loss ==================") stop_high = ta.highest(high, high_bars) stop_low = ta.lowest(low, low_bars) // --------- Stop loss source selection --------------- stoploss_type = input.string(defval = "Atr stop loss", title = "General stop loss source", group = "================== Stop loss ==================", options = ["Atr stop loss","Swing high/low"]) if stoploss_type == "Atr stop loss" shortStopLoss := source_atr + ta.atr(length_atr) * multiplier longStopLoss := source_atr - ta.atr(length_atr) * multiplier if candle_of_stoploss == "Past candle" and stoploss_type == "Atr stop loss" shortStopLoss := close[1] + ta.atr(length_atr)[1] * multiplier[1] longStopLoss := close[1] - ta.atr(length_atr)[1] * multiplier[1] if stoploss_type == "Swing high/low" shortStopLoss := stop_high longStopLoss := stop_low // ======================================== Add/withdraw money frequently (>>>Beta<<<) ============================================================== // Declare some variables ========================================================= var initial_capital = strategy.initial_capital var initial_capital_a = strategy.initial_capital var initial_capital_w = strategy.initial_capital var float capital_added = 0 var float balance = strategy.initial_capital var prev_month = 0 var float amount = 0 var add_frequency = 0 var withdraw_frequency = 0 // Choose how often the strategy adds money ========================================================= add_money_frequency = input.string("Monthly", title = "Choose how often would you add money", options = ["Monthly","Weekly","Daily","Yearly"], group = "=============== Adding money frequently ===============") amount_to_add = input.float(defval = 10, title = "How much you want to add ?", group = "=============== Adding money frequently ===============") if add_money_frequency == "Monthly" add_frequency := month if add_money_frequency == "Weekly" add_frequency := weekofyear if add_money_frequency == "Daily" add_frequency := dayofweek if add_money_frequency == "Yearly" add_frequency := year // Choose if you want to add money or not ==================================== add_money = input.string("No", title = "Add money from time to time ?", options = ["Yes","No"] , group = "=============== Adding money frequently ===============") if add_frequency != add_frequency[1] and add_money == "Yes" and date initial_capital_a += amount_to_add initial_capital += amount_to_add balance := strategy.netprofit + initial_capital // Choose how often the strategy withdraws money ========================================================= amount_to_withdraw = input.string("Fixed", title = "Withdraw based in fixed amounts or percentage of earnings", options = ["%","Fixed"], group = "=============== Withdraw money frequently ===============") amount_for_withdraw = input.float(defval = 2, title = "How much you want to withdraw (fixed amount) ?", group = "=============== Withdraw money frequently ===============") withdraw_money_frequency = input.string("Monthly", title = "Choose how often would you withdraw money", options = ["Monthly","Weekly","Daily","Yearly"], group = "=============== Withdraw money frequently ===============") // We use this for being able to choose the frequency of withdrawing money: if withdraw_money_frequency == "Monthly" withdraw_frequency := month if withdraw_money_frequency == "Weekly" withdraw_frequency := weekofyear if withdraw_money_frequency == "Daily" withdraw_frequency := dayofweek if withdraw_money_frequency == "Yearly" withdraw_frequency := year // Choose if you want to withdraw money or not ============================= withdraw_money = input.string("No", title = "Withdraw money from time to time ?", options = ["Yes","No"], group = "=============== Withdraw money frequently ===============") percentage_of_earnings = input.float(10, title = "Percentage of earnings", group = "=============== Withdraw money frequently ===============", tooltip = "Use this if withdraw is based in % of earnings") // Percentage of earnings: if withdraw_frequency != withdraw_frequency[1] and withdraw_money == "Yes" and amount_to_withdraw == "%" and date and strategy.netprofit>0 initial_capital_w -= strategy.netprofit * (percentage_of_earnings / 100) initial_capital -= strategy.netprofit * (percentage_of_earnings / 100) balance := strategy.netprofit + initial_capital // Fixed amount: if withdraw_frequency != withdraw_frequency[1] and withdraw_money == "Yes" and amount_to_withdraw == "Fixed" and date and strategy.netprofit>0 initial_capital_w -= amount_for_withdraw initial_capital -= amount_for_withdraw balance := strategy.netprofit + initial_capital // Logic if withdraw_money == "Yes" and add_money == "No" amount := balance if withdraw_money =="Yes" and add_money =="Yes" amount := balance if withdraw_money == "No" and add_money == "Yes" amount := balance if withdraw_money == "No" and add_money == "No" amount := strategy.equity // ============================================================================= ================================================================== // ------------- Money management -------------- strategy_contracts = amount / close distance_sl_long = -1 * (longStopLoss - close) / close distance_sl_short = (shortStopLoss - close) / close risk = input.float(2.5, '% Account risk per trade for backtesting', step=1, group = "============ Risk management for trades ============", tooltip = "Percentage of total account to risk per trade if fixed amounts is deactivated") long_amount = strategy_contracts * (risk / 100) / distance_sl_long short_amount = strategy_contracts * (risk / 100) / distance_sl_short leverage=input.bool(defval=true, title="Use leverage for backtesting ?", group = "============ Risk management for trades ============", tooltip = "If it is activated, there will be no monetary units or amount of assets limit for each operation (That is, each operation will not be a?ected by the initial / current capital since it would be using leverage). If it is deactivated, the monetary units or the amount of assets to use for each operation will be limited by the initial/current capital.") if not leverage and long_amount>strategy_contracts long_amount:=amount/close if not leverage and short_amount>strategy_contracts short_amount:=amount/close // ---- Fixed amounts ---- fixed_amounts = input.bool(defval = false, title = "Fixed amounts ?", group = "============ Risk management for trades ============", tooltip = "If you activate this, the backtester will use fixed amounts") fixed_amount_input = input.float(defval = 1000, title = "Fixed amount in usd", group = "============ Risk management for trades ============") if fixed_amounts long_amount := fixed_amount_input / close short_amount := fixed_amount_input / close // ---------- Risk management --------------- risk_reward_breakeven_long= input.float(title="Risk/reward for breakeven long", defval=1.0, step=0.1, group = "============ Risk management for trades ============") risk_reward_take_profit_long= input.float(title="Risk/reward for take profit long", defval=3.0, step=0.1, group = "============ Risk management for trades ============") risk_reward_breakeven_short= input.float(title="Risk/reward for break even short", defval=1.0, step=0.1, group = "============ Risk management for trades ============") risk_reward_take_profit_short= input.float(title="Risk/reward for take profit short", defval=3.0, step=0.1, group = "============ Risk management for trades ============") tp_percent=input.float(title="% of trade for first take profit", defval=50, step=5, group = "============ Risk management for trades ============", tooltip = "Closing percentage of the current position when the first take profit is reached.") // ============================================================================= ================================================================== bought = strategy.position_size > 0 sold = strategy.position_size < 0 buy = startLongTrade sell = startShortTrade var float total_commissions_value = 0 var float commission_value_l = 0 var float commission_value_s = 0 var float sl_long = na var float sl_short = na var float be_long = na var float be_short = na var float tp_long = na var float tp_short = na var int totaltrades = 0 close_withsupertrend = input.bool(defval = true, title = "Close positions with supertrend ?", group = "============ Positions management ============") closeshort_supertrend=ta.crossover(close, supertrend) closelong_supertrend=ta.crossunder(close, supertrend) if not bought be_long:=na sl_long:=na tp_long:=na if not sold be_short:=na sl_short:=na tp_short:=na long_positions = input.bool(defval = true, title = "Long positions ?", group = "============ Positions management ============") short_positions = input.bool(defval = true, title = "Short positions ?", group = "============ Positions management ============") use_takeprofit = input.bool(defval = true, title = "Use take profit ?", group = "============ Risk management for trades ============") use_breakeven = input.bool(defval = true, title = "Use break even ?", group = "============ Risk management for trades ============") close_only_tp = input.bool(defval = false, title = "Close just with take profit ?", group = "============ Risk management for trades ============", tooltip = "Activate if you just want to exit from a position until reaching take profit or stop loss. If it�s activated, change % of closing by tp to 100%") ema_filter_long = input.bool(defval = true, title = "Ema filter for long positions ?", group = "============ Positions management ============", tooltip = "Activate if you just want to long above 200 ema") ema_filter_short = input.bool(defval = true, title = "Ema filter for short positions ?", group = "============ Positions management ============", tooltip = "Activate if you just want to short under 200 ema") commission_percent = input.float(0.03, title = "Commission value in %", group = "============ Positions management ============", tooltip = "Set the % of commission. For example, when you enter into a position, you have a commission of 0.04% per entry and 0.04% per exit. You have also to change this value in properties for getting a real return in backtest. (in this case, 0.04%)") if fixed_amounts commission_value_l := (close * (long_amount) * (commission_percent/100)) commission_value_s := (close * (short_amount) * (commission_percent/100)) if not fixed_amounts commission_value_l := (close * ((strategy_contracts * (risk / 100)) / distance_sl_long) * (commission_percent/100)) commission_value_s := (close * ((strategy_contracts * (risk / 100)) / distance_sl_short) * (commission_percent/100)) // ======================= Strategy ============================================================================= ====================================== // Long position with take profit float test = 123.12 var string tradeNameLong = na var string tradeNameShort = na //alert("Test Long " + str.tostring(test)) // Testing //long_amount := initial_actual_capital / close //short_amount := initial_actual_capital / close strategyEntryHappened = false // 13.09.24 Attention strict useTakeProfit maybe changes backtesting results if (use_takeprofit) if not bought and buy and date and long_positions and inSession(timeSession) and ( ( bullish and ema_filter_long) or not ema_filter_long ) and not strategy.position_size sl_long:=longStopLoss long_stoploss_distance = close - longStopLoss be_long := close + long_stoploss_distance * risk_reward_breakeven_long tp_long:=close+(long_stoploss_distance*risk_reward_take_profit_long) total_commissions_value += commission_value_l strategyEntryHappened := true tradeNameLong := syminfo.tickerid + "_" + str.tostring(time("")) + str.tostring(math.round(math.random(10000,99999,22342))) strategy.entry('L', strategy.long, long_amount, alert_message = "Long " + tradeNameLong + " " + str.tostring(long_amount) + " " + str.tostring(sl_long) + " " + str.tostring(tp_long) + " " + str.tostring(tp_percent) + " " + str.tostring(close)) strategy.exit("Tp", "L", stop=sl_long, limit=tp_long, qty_percent=tp_percent, alert_message = "Longexit " + tradeNameLong) strategy.exit('Exit', 'L', stop=sl_long, alert_message = "Longexit " + tradeNameLong + "-2") if bought and high > be_long and use_breakeven sl_long := strategy.position_avg_price strategy.exit("Tp", "L", stop=sl_long, limit=tp_long, qty_percent=tp_percent, alert_message = "Longexit " + tradeNameLong) strategy.exit('Exit', 'L', stop=sl_long, alert_message = "Longexit " + tradeNameLong + "-2") alert("Tpsl " + tradeNameLong + " " + str.tostring(long_amount) + " " + str.tostring(sl_long) + " " + str.tostring(tp_long) + " " + str.tostring(tp_percent) + " " + str.tostring(close)) if bought and sell and strategy.openprofit>0 and not close_only_tp or bought and closelong_supertrend and close_withsupertrend and strategy.openprofit>0 and not close_only_tp strategy.close("L", comment="CL") alert("Close " + tradeNameLong ) balance := balance + strategy.openprofit plot(strategyEntryHappened ? 1 : 0, title="strategyEntryHappened", display = display.data_window) plot(bought and high > be_long and use_breakeven ? 1 : 0, title="bought and high > be_long and use_breakeven", display = display.data_window) // Long position without take profit if (not use_takeprofit) if not bought and buy and date and long_positions and inSession(timeSession) and ( ( bullish and ema_filter_long) or not ema_filter_long ) and not strategy.position_size sl_long:=longStopLoss long_stoploss_distance = close - longStopLoss be_long := close + long_stoploss_distance * risk_reward_breakeven_long total_commissions_value += commission_value_l tradeNameLong := syminfo.tickerid + "_" + str.tostring(time("")) + str.tostring(math.round(math.random(10000,99999,22342))) strategy.entry('L', strategy.long, long_amount, alert_message = "Long " + tradeNameLong + " " + str.tostring(long_amount) + " " + str.tostring(0) + " " + str.tostring(tp_long) + " " + str.tostring(tp_percent) + " " + str.tostring(close)) strategy.exit('Exit', 'L', stop=sl_long, alert_message = "Longexit " + tradeNameLong) if bought and high > be_long and use_breakeven sl_long := strategy.position_avg_price strategy.exit('Exit', 'L', stop=sl_long, alert_message = "Longexit " + tradeNameLong) alert("Tpsl " + tradeNameLong + " " + str.tostring(long_amount) + " " + str.tostring(sl_long) + " " + str.tostring(0) + " " + str.tostring(tp_percent) + " " + str.tostring(close)) if bought and sell and strategy.openprofit>0 strategy.close("L", comment="CL", alert_message = "Close " + tradeNameLong) balance := balance + strategy.openprofit // Short position with take profit if (use_takeprofit) if not sold and sell and date and short_positions and inSession(timeSession) and ( ( bearish and ema_filter_short) or not ema_filter_short ) and not strategy.position_size sl_short:=shortStopLoss short_stoploss_distance=shortStopLoss - close be_short:=((short_stoploss_distance*risk_reward_breakeven_short)-close)*-1 tp_short:=((short_stoploss_distance*risk_reward_take_profit_short)-close)*-1 total_commissions_value += commission_value_s tradeNameShort := syminfo.tickerid + "_" + str.tostring(time("")) + str.tostring(math.round(math.random(10000,99999,22342))) strategy.entry("S", strategy.short, short_amount, alert_message = "Short " + tradeNameShort + " " + str.tostring(short_amount) + " " + str.tostring(sl_short) + " " + str.tostring(tp_short) + " " + str.tostring(tp_percent) + " " + str.tostring(close)) strategy.exit("Tp", "S", stop=sl_short, limit=tp_short, qty_percent=tp_percent, alert_message = "Shortexit " + tradeNameShort) strategy.exit("Exit", "S", stop=sl_short, alert_message = "Shortexit " + tradeNameShort + "-2") if sold and low < be_short and use_breakeven sl_short:=strategy.position_avg_price strategy.exit("Tp", "S", stop=sl_short, limit=tp_short, qty_percent=tp_percent, alert_message = "Shortexit " + tradeNameShort) strategy.exit("Exit", "S", stop=sl_short, alert_message = "Shortexit " + tradeNameShort + "-2") alert("Tpsl " + tradeNameShort + " " + str.tostring(short_amount) + " " + str.tostring(sl_short) + " " + str.tostring(tp_short) + " " + str.tostring(tp_percent) + " " + str.tostring(close)) if sold and buy and strategy.openprofit>0 and not close_only_tp or sold and closeshort_supertrend and close_withsupertrend and strategy.openprofit>0 and not close_only_tp strategy.close("S", comment="CS") alert("Close " + tradeNameShort ) balance := balance + strategy.openprofit // Short position without take profit if ( not use_takeprofit) if not sold and sell and date and short_positions and inSession(timeSession) and ( ( bearish and ema_filter_short) or not ema_filter_short ) and not strategy.position_size sl_short:=shortStopLoss short_stoploss_distance=shortStopLoss - close be_short:=((short_stoploss_distance*risk_reward_breakeven_short)-close)*-1 total_commissions_value += commission_value_s tradeNameShort := syminfo.tickerid + "_" + str.tostring(time("")) + str.tostring(math.round(math.random(10000,99999,22342))) strategy.entry("S", strategy.short, short_amount, alert_message = "Short " + tradeNameShort + " " + str.tostring(short_amount) + " " + str.tostring(sl_short) + " " + str.tostring(0) + " " + str.tostring(tp_percent) + " " + str.tostring(close)) strategy.exit("Exit", "S", stop=sl_short, alert_message = "Shortexit " + tradeNameShort) if sold and low < be_short and use_breakeven sl_short:=strategy.position_avg_price strategy.exit("Exit", "S", stop=sl_short, alert_message = "Shortexit " + tradeNameShort) alert("Tpsl " + tradeNameShort + " " + str.tostring(short_amount) + " " + str.tostring(sl_short) + " " + str.tostring(0) + " " + str.tostring(tp_percent) + " " + str.tostring(close)) if sold and buy and strategy.openprofit>0 strategy.close("S", comment="CS", alert_message = "Close " + tradeNameShort) balance := balance + strategy.openprofit // ============================================================================= ================================================================== plot(sl_long,title="sl_long", display = display.data_window) plot(tp_long,title="tp_long", display = display.data_window) plot(sl_short,title="sl_short", display = display.data_window) plot(tp_short,title="tp_short", display = display.data_window) // Long position with ema filter // With take profit //if not bought and buy and date and long_positions and inSession(timeSession) and use_takeprofit and bullish and ema_filter_long // sl_long:=longStopLoss // long_stoploss_distance = close - longStopLoss // be_long := close + long_stoploss_distance * risk_reward_breakeven_long // tp_long:=close+(long_stoploss_distance*risk_reward_take_profit_long) // total_commissions_value += commission_value_l // strategy.entry('L', strategy.long, long_amount, alert_message = "Long") // strategy.exit("Tp", "L", stop=sl_long, limit=tp_long, qty_percent=tp_percent) // strategy.exit('Exit', 'L', stop=sl_long) //if bought and high > be_long and use_breakeven // sl_long := strategy.position_avg_price // strategy.exit("Tp", "L", stop=sl_long, limit=tp_long, qty_percent=tp_percent) // strategy.exit('Exit', 'L', stop=sl_long) //if bought and sell and strategy.openprofit>0 and not close_only_tp or bought and closelong_supertrend and close_withsupertrend and strategy.openprofit>0 and not close_only_tp // strategy.close("L", comment="CL") // balance := balance + strategy.openprofit// //// Without take profit// //if not bought and buy and date and long_positions and inSession(timeSession) and bullish and ema_filter_long // sl_long:=longStopLoss // long_stoploss_distance = close - longStopLoss // be_long := close + long_stoploss_distance * risk_reward_breakeven_long // total_commissions_value += commission_value_l // strategy.entry('L', strategy.long, long_amount, alert_message = "Long") // strategy.exit('Exit', 'L', stop=sl_long) //if bought and high > be_long and use_breakeven // sl_long := strategy.position_avg_price // strategy.exit('Exit', 'L', stop=sl_long) //if bought and sell and strategy.openprofit>0 // strategy.close("L", comment="CL") // balance := balance + strategy.openprofit// //// Short positon with ema filter // //// With take profit //if not sold and sell and date and short_positions and inSession(timeSession) and use_takeprofit and bearish and ema_filter_short // sl_short:=shortStopLoss // short_stoploss_distance=shortStopLoss - close // be_short:=((short_stoploss_distance*risk_reward_breakeven_short)-close)*-1 // tp_short:=((short_stoploss_distance*risk_reward_take_profit_short)-close)*-1 // total_commissions_value += commission_value_s // strategy.entry("S", strategy.short, short_amount, alert_message = "Short") // strategy.exit("Tp", "S", stop=sl_short, limit=tp_short, qty_percent=tp_percent) // strategy.exit("Exit", "S", stop=sl_short) //if sold and low < be_short and use_breakeven // sl_short:=strategy.position_avg_price // strategy.exit("Tp", "S", stop=sl_short, limit=tp_short, qty_percent=tp_percent) // strategy.exit("Exit", "S", stop=sl_short) //if sold and buy and strategy.openprofit>0 and not close_only_tp or sold and closeshort_supertrend and close_withsupertrend and strategy.openprofit>0 and not close_only_tp // strategy.close("S", comment="CS") // balance := balance + strategy.openprofit// //// Without take profit// //if not sold and sell and date and short_positions and inSession(timeSession) and bearish and ema_filter_short // sl_short:=shortStopLoss // short_stoploss_distance=shortStopLoss - close // be_short:=((short_stoploss_distance*risk_reward_breakeven_short)-close)*-1 // total_commissions_value += commission_value_s // strategy.entry("S", strategy.short, short_amount, alert_message = "Short") // strategy.exit("Exit", "S", stop=sl_short) //if sold and low < be_short and use_breakeven // sl_short:=strategy.position_avg_price // strategy.exit("Exit", "S", stop=sl_short) //if sold and buy and strategy.openprofit>0 // strategy.close("S", comment="CS") // balance := balance + strategy.openprofit // ============================================================================= ================================================================== // ---------- Draw positions and signals on chart (strategy as an indicator) ------------- if high>tp_long tp_long:=na if lowbe_long be_long:=na if lowbe_short and show_position_on_chart and use_breakeven? be_short : na , style = plot.style_linebr, color = color.new(#1fc9fd, 60), linewidth = 1, display = display.all - display.status_line - display.price_scale) show_tpbe_on_chart = input.bool(defval=true, title="Draw first take profit/breakeven price on chart ?", group = "============ Appearance ============", tooltip = "Activate to display take profit and breakeven price. It appears as a green point in the chart") long_stoploss_distance = close - longStopLoss short_stoploss_distance=shortStopLoss - close be_long_plot = close + long_stoploss_distance * risk_reward_breakeven_long be_short_plot =((short_stoploss_distance*risk_reward_breakeven_short)-close)*-1 tp_long_plot = close+(long_stoploss_distance*risk_reward_take_profit_long) tp_short_plot = ((short_stoploss_distance*risk_reward_take_profit_short)-close)*-1 plot(show_tpbe_on_chart and buy and use_breakeven and bullish and ema_filter_long or show_tpbe_on_chart and buy and use_breakeven and not ema_filter_long ? be_long_plot : na, color=color.new(#1fc9fd, 10), style = plot.style_circles, linewidth = 2, display = display.all - display.price_scale) plot(show_tpbe_on_chart and sell and use_breakeven and bearish and ema_filter_short or show_tpbe_on_chart and sell and use_breakeven and not ema_filter_short ? be_short_plot : na, color=color.new(#1fc9fd, 10), style = plot.style_circles, linewidth = 2, display = display.all - display.price_scale) plot(show_tpbe_on_chart and buy and use_takeprofit and bullish and ema_filter_long or show_tpbe_on_chart and buy and use_takeprofit and not ema_filter_long? tp_long_plot : na, color=color.new(#4cd350, 10), style = plot.style_circles, linewidth = 2, display = display.all - display.price_scale) plot(show_tpbe_on_chart and sell and use_takeprofit and bearish and ema_filter_short or show_tpbe_on_chart and sell and use_takeprofit and not ema_filter_short? tp_short_plot : na, color=color.new(#4cd350, 10), style = plot.style_circles, linewidth = 2, display = display.all - display.price_scale) plot(show_stoploss and buy and bullish and ema_filter_long or show_stoploss and buy and not ema_filter_long ? longStopLoss : na, title = "stop loss long", color = color.white, style = plot.style_circles, linewidth = 2) plot(show_stoploss and sell and bearish and ema_filter_short or show_stoploss and sell and not ema_filter_long ? shortStopLoss : na, title = "stop loss short", color = color.white, style = plot.style_circles, linewidth = 2) position_profit_long = plot(bought and show_position_on_chart and strategy.openprofit>0 ? close : na, style = plot.style_linebr, color = color.new(#4cd350, 10), linewidth = 1, display = display.all - display.status_line - display.price_scale) position_profit_short = plot(sold and show_position_on_chart and strategy.openprofit>0 ? close : na, style = plot.style_linebr, color = color.new(#4cd350, 10), linewidth = 1, display = display.all - display.status_line - display.price_scale) fill(plot1 = position_price, plot2 = position_profit_long, color = color.new(#4cd350, 90)) fill(plot1 = position_price, plot2 = position_profit_short, color = color.new(#4cd350, 90)) fill(plot1 = position_price, plot2 = sl_long_price, color = color.new(color.red,90)) fill(plot1 = position_price, plot2 = sl_short_price, color = color.new(color.red,90)) fill(plot1 = position_price, plot2 = tp_long_price, color = color.new(color.green,90)) fill(plot1 = position_price, plot2 = tp_short_price, color = color.new(color.green,90)) show_signals = input.bool(defval=true, title="Show signals on chart ?", group = "============ Appearance ============") plotshape(show_signals and buy and bullish and ema_filter_long or show_signals and buy and not ema_filter_long ? low : na, title='Buy', text='Buy', style=shape.labelup, location=location.belowbar, color=color.new(color.green, 20), textcolor=color.new(color.white, 0), size=size.tiny , display = display.all - display.price_scale - display.status_line) plotshape(show_signals and sell and bearish and ema_filter_long or show_signals and sell and not ema_filter_short ? high : na, title='Sell', text='Sell', style=shape.labeldown, location=location.abovebar, color=color.new(color.red, 20), textcolor=color.new(color.white, 0), size=size.tiny, display = display.all - display.price_scale - display.status_line) plotshape(show_signals and closelong_supertrend and close_withsupertrend and bought or show_signals and sell and bought ? low : na, title='Cl Buy', text='Cl Buy', style=shape.labelup, location=location.belowbar, color=color.new(#4cafaf, 30), textcolor=color.new(color.white, 0), size=size.tiny , display = display.all - display.price_scale - display.status_line) plotshape(show_signals and closeshort_supertrend and close_withsupertrend and sold or show_signals and buy and bought? high : na, title='Cl Buy', text='Cl sell', style=shape.labeldown, location=location.abovebar, color=color.new(#4cafaf, 30), textcolor=color.new(color.white, 0), size=size.tiny, display = display.all - display.price_scale - display.status_line) // ============================================================================= ================================================================== // --------------- Positions amount calculator ------------- contracts_amount_c = initial_actual_capital / close distance_sl_long_c = -1 * (longStopLoss - close) / close distance_sl_short_c = (shortStopLoss - close) / close long_amount_c = close * (contracts_amount_c * (risk_c / 100) / distance_sl_long_c) short_amount_c = close * (contracts_amount_c * (risk_c / 100) / distance_sl_short_c) long_amount_lev = close * (contracts_amount_c * (risk_c / 100) / distance_sl_long_c) short_amount_lev = close * (contracts_amount_c * (risk_c / 100) / distance_sl_short_c) leverage_for_calculator=input.bool(defval=true, title="Use leverage ?", group = "============ Position amount calculator ===========", tooltip = "If it is activated, there will be no monetary units or amount of assets limit for each operation (That is, each operation will not be a?ected by the initial / current capital since it would be using leverage). If it is deactivated, the monetary units or the amount of assets to use for each operation will be limited by the initial/current capital.") if not leverage_for_calculator and long_amount_lev>initial_actual_capital long_amount_lev:=initial_actual_capital if not leverage_for_calculator and short_amount_lev>initial_actual_capital short_amount_lev:=initial_actual_capital plot(buy and leverage_for_calculator ? long_amount_c : na, color = color.rgb(255, 230, 0), display = display.all - display.pane - display.price_scale) plot(sell and leverage_for_calculator ? short_amount_c : na, color = color.rgb(255, 230, 0), display = display.all - display.pane - display.price_scale) plot(buy and not leverage_for_calculator ? long_amount_lev : na, color = color.rgb(255, 230, 0), display = display.all - display.pane - display.price_scale) plot(sell and not leverage_for_calculator ? short_amount_lev : na, color = color.rgb(255, 230, 0), display = display.all - display.pane - display.price_scale) // ============================================================================= ================================================================== // ===================== Drawing stats about add and withdraw money frequently and others on chart =================== if not bought and buy and date and not ema_filter_long totaltrades += 1 if not sold and sell and date and not ema_filter_short totaltrades += 1 if not bought and buy and date and bearish and ema_filter_long totaltrades += 0 if not sold and sell and date and bullish and ema_filter_short totaltrades += 0 if not bought and buy and date and bullish and ema_filter_long totaltrades += 1 if not sold and sell and date and bearish and ema_filter_short totaltrades += 1 total_money_added = initial_capital_a - strategy.initial_capital total_money_withdrawn = initial_capital_w - strategy.initial_capital final_money = total_money_added + strategy.netprofit + strategy.initial_capital - total_money_withdrawn // or current money avaliable truncate(_number, _decimalPlaces) => _factor = math.pow(10, _decimalPlaces) int(_number * _factor) / _factor draw_stats = input.bool(true, "Show stats from add/withdraw money frequently ?", group = "============ Appearance ============") // Prepare stats table var table testTable = table.new(position.top_right, 5, 2, border_width=2, frame_color = color.rgb(0, 0, 0), frame_width = 2) f_fillCell(_table, _column, _row, _title, _value, _bgcolor, _txtcolor,_size,_tooltip) => _cellText = _title + "\n" + _value table.cell(_table, _column, _row, _cellText, bgcolor=_bgcolor, text_color=_txtcolor, text_size=_size, tooltip = _tooltip) // Draw stats table var bgcolor = color.new(#2f5cda, 50) var bgcolor2 = color.new(color.green, 50) var bgcolor3 = color.new(color.red,50) if draw_stats if barstate.islastconfirmedhistory f_fillCell(testTable, 0, 0, "Final/current money:", "$" + str.tostring(truncate(final_money,2)), bgcolor, color.white, _size = size.normal, _tooltip = "Total money added + total return from strategy + initial capital - total money withdrawn") f_fillCell(testTable, 0, 1, "Total money added:", "$" + str.tostring(truncate(total_money_added,2)), bgcolor, color.white, _size = size.normal, _tooltip = "Sum of total money added at the end of the date") f_fillCell(testTable, 1, 0, "Total money withdrawn:", "$" + str.tostring(truncate(total_money_withdrawn*-1,2)), bgcolor, color.white, _size = size.normal, _tooltip = "Sum of total money withdrawn at the end of the date") f_fillCell(testTable, 1, 1, "Total return:", "$" + str.tostring(truncate(strategy.netprofit,2)), strategy.netprofit > 0 ? bgcolor2 : bgcolor3 , color.white, _size = size.normal, _tooltip = "Total return from strategy until the end of the date (it could be di?erent from default backtesting if last position have not been closed completly)") f_fillCell(testTable, 2, 0, "Total trades:", "" + str.tostring(truncate(totaltrades,2)), bgcolor, color.white, _size = size.normal, _tooltip = "Sum of real total trades. The value from default backtester it�s not precise") f_fillCell(testTable, 2, 1, "Total commissions value:", "" + str.tostring(truncate(total_commissions_value,2)), bgcolor, color.white, _size = size.normal, _tooltip = "Sum of commissions value from all trades. You must set the % value in the script settings (Positions management).") // ===================================================================== bool showPrediction = input.bool(false) plot(showPrediction ? prediction : na, title="prediction", color = color.red)